Abstract
Recent progress in aspect-level sentiment classification has been propelled by the incorporation of graph neural networks (GNNs) leveraging syntactic structures, particularly dependency trees. Nevertheless, the performance of these models is often hampered by the innate inaccuracies of parsing algorithms. To mitigate this challenge, we introduce SynthFusion, an innovative graph ensemble method that amalgamates predictions from multiple parsers. This strategy blends diverse dependency relations prior to the application of GNNs, enhancing robustness against parsing errors while avoiding extra computational burdens. SynthFusion circumvents the pitfalls of overparameterization and diminishes the risk of overfitting, prevalent in models with stacked GNN layers, by optimizing graph connectivity. Our empirical evaluations on the SemEval14 and Twitter14 datasets affirm that SynthFusion not only outshines models reliant on single dependency trees but also eclipses alternative ensemble techniques, achieving this without an escalation in model complexity.
Abstract (translated)
近年来,面向 aspect-level 情感分类的进展主要是由利用语义结构,特别是依赖关系树,进行图神经网络(GNNs)的引入所带来的。然而,这些模型的性能通常受到解析算法的固有不准确性所困扰。为了应对这一挑战,我们引入了SynthFusion,一种创新的图集成方法,它将多个解析器的预测进行集成。这种策略在应用GNNs之前融合了不同的依赖关系,提高了对解析错误时的鲁棒性,同时避免了对计算开销的额外累积。通过优化图的连接性,SynthFusion绕过了堆叠GNN层的模型的陷阱,减少了超参数设置和不准确的风险,同时实现了在没有模型复杂度增加的情况下提高性能的目标。我们对SemEval14和Twitter14数据集的实证评估证实,SynthFusion不仅超越了依赖单个依赖树模型的模型,而且超过了其他集成方法,实现了在没有模型复杂度增加的情况下实现这一目标。
URL
https://arxiv.org/abs/2312.03738
